Comprehensive Metabolomic Fingerprinting Combined with Chemometrics Identifies Species- and Variety-Specific Variation of Medicinal Herbs: An Ocimum Study

• Published in: Metabolites Vol. 13; no. 1; p. 122
• Main Authors: Rai, Abhishek Kumar, Khan, Samreen, Kumar, Akhilesh, Dubey, Basant Kumar, Lal, R K, Tiwari, Ashutosh, Trivedi, Prabodh Kumar, Elliott, Christopher T, Ch, Ratnasekhar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.01.2023; MDPI
• Subjects: chemometrics; Chromatography; Classification; Fingerprinting; Flavonoids; Food; Herbal medicine; Herbs; Mass spectrometry; medicinal herbs; Medicinal plants; Metabolism; Metabolites; metabolomic fingerprinting; Metabolomics; Natural products; NMR; Nuclear magnetic resonance; Ocimum; Ocimum species and varieties; Pharmaceuticals; Principal components analysis; Quality control; Scientific imaging; Species; Taxonomy; Variation; United States > US; metabolomic fingerprinting; Ocimum species and varieties; chemometrics; medicinal herbs
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo13010122

Identification of plant species is a crucial process in natural products. , often referred to as the queen of herbs, is one of the most versatile and globally used medicinal herbs for various health benefits due to it having a wide variety of pharmacological activities. Despite there being significant global demand for this medicinal herb, rapid and comprehensive metabolomic fingerprinting approaches for species- and variety-specific classification are limited. In this study, metabolomic fingerprinting of five species ( L., L., , , and Hybrid Tulsi) and their varieties was performed using LC-MS, GC-MS, and the rapid fingerprinting approach FT-NIR combined with chemometrics. The aim was to distinguish the species- and variety-specific variation with a view toward developing a quality assessment of species. Discrimination of species and varieties was achieved using principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA), data-driven soft independent modelling of class analogy (DD-SIMCA), random forest, and K-nearest neighbours with specificity of 98% and sensitivity of 99%. Phenolics and flavonoids were found to be major contributing markers for species-specific variation. The present study established comprehensive metabolomic fingerprinting consisting of rapid screening and confirmatory approaches as a highly efficient means to identify the species and variety of being able to be applied for the quality assessment of other natural medicinal herbs.